Methods for safety management of compressors in smart gas pipeline network and internet of things systems thereof

ABSTRACT

The embodiments of the present disclosure provide a method for safety management of a compressor in a smart gas pipeline network and an Internet of Things system thereof. The method is implemented based on a smart gas safety management platform of an Internet of Things system for safety management of a compressor in a smart gas pipeline network. The method comprises: obtaining sound data and a target vibration feature of a gas compressor, and determining a target sound feature based on the sound data; obtaining gas data and device data, and determining a standard sound feature and a standard vibration feature based on the gas data and the device data; and predicting whether there is a safety hazard in the gas compressor based on the target vibration feature and the standard vibration feature, or based on the target sound feature and the standard sound feature.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.202211629717.3, filed on Dec. 19, 2022, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to the field of the Internet of Thingsand cloud platforms, in particular to a method for safety management ofa compressor in a smart gas pipeline network and an Internet of Thingssystem thereof.

BACKGROUND

With the increasing demand for natural gas energy in daily life ofpeople, it is very important to improve the safety management of acompressor in the smart gas pipeline network, ensure the safety ofoperation, and realize the safety management of the gas pipelinenetwork. During the usage process of the gas compressor, various faultsmay occur in the gas compressor due to loose parts, excessive impuritiesand accumulation of impurities in natural gas, parts aging, or the like.However, the existing fault diagnosis method for the gas compressor iseasily affected by background noise, which cannot realize the safetymanagement of the gas pipeline network. How to accurately obtain asafety hazard of the gas compressor and realize the safety management ofthe gas pipeline network is an urgent problem to be solved.

Therefore, it is hoped to provide a method for safety management of acompressor in a smart gas pipeline network and an Internet of Thingssystem thereof, improving the efficiency of safety management of the gaspipeline network using the Internet of Things and a cloud platform,while ensuring the accuracy of obtaining the safety hazard of the gascompressor.

SUMMARY

One or more embodiments of the present disclosure provide a method forsafety management of a compressor in a smart gas pipeline network. Themethod is implemented based on a smart gas safety management platform ofan Internet of Things system for safety management of a compressor in asmart gas pipeline network. The method for safety management of thecompressor in the smart gas pipeline network includes: obtaining sounddata and a target vibration feature of a gas compressor, and determininga target sound feature based on the sound data; obtaining gas data anddevice data, and determining a standard sound feature and a standardvibration feature based on the gas data and the device data; andpredicting whether there is a safety hazard in the gas compressor basedon the target vibration feature and the standard vibration feature, orbased on the target sound feature and the standard sound feature.

One or more embodiments of the present disclosure provide an Internet ofThings system for safety management of a compressor in a smart gaspipeline network. The system comprises: a smart gas safety managementplatform. The smart gas safety management platform is configured toperform operations including: obtaining sound data and a targetvibration feature of a gas compressor, and determining a target soundfeature based on the sound data; obtaining gas data and device data, anddetermining a standard sound feature and a standard vibration featurebased on the gas data and the device data; and predicting whether thereis a safety hazard in the gas compressor based on the target vibrationfeature and the standard vibration feature, or based on the target soundfeature and the standard sound feature.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, wherein:

FIG. 1 is a schematic diagram of an Internet of Things system for safetymanagement of a compressor in a smart gas pipeline network according tosome embodiments of the present disclosure;

FIG. 2 is a flowchart illustrating an exemplary method for safetymanagement of a compressor in a smart gas pipeline network according tosome embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating an exemplary process fordetermining a target sound feature according to some embodiments of thepresent disclosure;

FIG. 4 is a schematic diagram illustrating an exemplary process forpredicting a safety hazard probability of a gas compressor according tosome embodiments of the present disclosure; and

FIG. 5 is a schematic diagram illustrating an exemplary process fortraining a hazard model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The technical solutions of the present disclosure embodiments will bemore clearly described below, and the accompanying drawings need to beconfigured in the description of the embodiments will be brieflydescribed below. Obviously, drawings described below are only someexamples or embodiments of the present disclosure. Those skilled in theart, without further creative efforts, may apply the present disclosureto other similar scenarios according to these drawings. Unless obviouslyobtained from the context or the context illustrates otherwise, the samenumeral in the drawings refers to the same structure or operation.

It should be understood that the “system”, “device”, “unit”, and/or“module” used herein are one method to distinguish different components,elements, parts, sections, or assemblies of different levels inascending order. However, the terms may be displaced by otherexpressions if they may achieve the same purpose

As shown in the present disclosure and claims, unless the contextclearly prompts the exception, “a”, “one”, and/or “the” is notspecifically singular, and the plural may be included. It will befurther understood that the terms “comprise,” “comprises,” and/or“comprising,” “include,” “includes,” and/or “including,” when used inthe present disclosure, specify the presence of stated steps andelements, but do not preclude the presence or addition of one or moreother steps and elements thereof.

The flowcharts are used in present disclosure to illustrate theoperations performed by the system according to the embodiment of thepresent disclosure. It should be understood that the front or rearoperation is not necessarily performed in order to accurately. Instead,the operations may be processed in reverse order or simultaneously.Moreover, one or more other operations may be added to the flowcharts,or one or more operations may be removed from the flowcharts.

FIG. 1 is a schematic diagram of an Internet of Things system for safetymanagement of a compressor in a smart gas pipeline network according tosome embodiments of the present disclosure.

The Internet of Things system is an information processing system thatincludes part or all of a user platform, a service platform, amanagement platform, a sensor network platform, and an object platform.The user platform is a functional platform configured to obtain userperceptual information and generate control information. The serviceplatform may be configured to connect the management platform and theuser platform and play functions of perceptual information servicecommunication and control information service communication. Themanagement platform may harmonize and coordinate the connection andcooperation between various functional platforms (such as the userplatform and the service platform). The management platform may gatherthe information of the Internet of Things operation system and mayprovide functions of perception management and control management forthe Internet of Things operation system. The sensor network platform isa functional platform for managing sensor communication. In someembodiments, the sensor network platform may be connected with themanagement platform and the object platform to realize functions ofperceptual information sensor communication and control informationsensor communication. The object platform is a functional platform forgenerating the perceptual information.

The processing of the information in the Internet of Things system maybe divided into a processing flow of the user perceptual information anda processing flow of the control information. The control informationmay be information generated based on the user perceptual information.In some embodiments, the control information may include the user demandcontrol information, and the user perceptual information may includeuser query information. The processing of the perceptual information maybe that the perceptual information is obtained by the object platformand transmitted to the management platform through the sensor networkplatform. The user demand control information may be transmitted fromthe management platform to the user platform through the serviceplatform, thereby realizing a control of sending prompt information.

As shown in FIG. 1 , the Internet of Things system for safety managementof the compressor in the smart gas pipeline network includes a smart gasuser platform 110, a smart gas service platform 120, a smart gas safetymanagement platform 130, a smart gas pipeline network device sensornetwork platform 140, and a smart gas pipeline network device objectplatform 150 that interact in sequence.

The smart gas user platform 110 is a user-oriented platform that may beconfigured to interact with a user. The user may be a gas user, asupervisory user, or the like. For example, the gas user may be aresidential gas user, a commercial gas user, an industrial gas user, orthe like. The supervisory user may be a person in charge of asupervisory part of gas safety, or the like. In some embodiments, thesmart gas user platform is configured as a terminal device. For example,the terminal device may include a mobile device, a tablet computer, orthe like, or any combination thereof. In some embodiments, the smart gasuser platform 110 may be configured to receive information and/orinstructions. In some embodiments, the smart gas user platform mayinclude a gas user sub-platform, a supervisory user sub-platform, or thelike.

The gas user sub-platform is configured to feedback gas usageinformation for the gas user (e.g., a gas consumer, etc.). For example,the gas user sub-platform may provide the gas user with information,such as gas usage amounts, gas indications, and gas costs. In someembodiments, the gas user sub-platform may correspond to and interactwith a smart gas use service sub-platform to obtain services for safegas use.

The supervisory user sub-platform is configured to supervise theoperation of the entire Internet of Things system for the supervisoryuser (e.g., a gas company, etc.). For example, the supervisory usersub-platform may supervise whether the pipeline network is reasonable,whether a related device is faulty, etc. In some embodiments, thesupervisory user sub-platform may correspond to and interact with asmart supervision service sub-platform to obtain services required bysafety supervision.

In some embodiments, the smart gas user platform may also push thesafety management information of the gas compressor to relevant users,so that the relevant users may deal with relevant problems and maintainrelevant devices in time. The safety management information of the gascompressor may include operation information of the gas compressor andgas transmission monitoring information of the gas pipeline. Theoperation information of the gas compressor may include variousindicators of the compressor, such as a sound feature and vibrationfrequency of the gas compressor, etc. The gas transmission monitoringinformation of the gas pipeline may include monitoring information of agas monitoring device, such as a gas flow, gas composition, a gaspressure, or the like.

In some embodiments, the smart gas user platform may be configured tointeract downward with the smart gas service platform. For example, thesmart gas user platform may issue a query instruction for safetymanagement information of gas pipeline network device to the smart gasservice platform, and receive the safety management information of gaspipeline network device uploaded by the smart gas service platform.

The smart gas service platform 120 is a platform for providing userswith gas-related services (e.g., query services, etc.). In someembodiments, the smart gas service platform may include a smart gas useservice sub-platform, a smart supervision service sub-platform, or thelike.

The smart gas use service sub-platform may correspond to the gas usersub-platform to provide the gas user with services, such as safe gas useservices and information inquiries about gas use. For example, the smartgas use service sub-platform may provide the gas user with a reminderservice for safe gas use. As another example, the gas user may queryinformation through the smart gas use service sub-platform, such as gasusage amounts and gas costs.

The smart supervision service sub-platform may correspond to thesupervisory user sub-platform, and provide the supervision user withservices required by safety supervision. For example, the supervisionuser may query the safety management information of the compressor andgas pipeline information in the smart gas pipeline network through thesmart supervision service sub-platform.

In some embodiments, the smart gas service platform may be configured tointeract upward with the smart gas user platform. For example, the smartgas service platform may receive the query instruction for the safetymanagement information of gas pipeline network device issued by thesmart gas user platform; upload the safety information of the gascompressor to the smart gas user platform, or the like. In someembodiments, the smart gas service platform may also be configured tointeract downward with the smart gas safety management platform. Forexample, the smart gas service platform may issue the query instructionfor the safety management information of gas pipeline network device tothe smart gas safety management platform, and receive the safetymanagement information of gas pipeline network device uploaded by thesmart gas safety management platform.

The smart gas safety management platform 130 is a platform forperforming safety management and monitoring on the gas-related device(e.g., the gas compressor). For example, the smart gas safety managementplatform may predict a safety hazard of the gas compressor based on thesafety management information of the gas compressor. In someembodiments, the smart gas safety management platform may include asmart gas pipeline network safety management sub-platform and a smartgas data center.

The smart gas pipeline network safety management sub-platform is aplatform for performing safety management and maintenance on the gaspipeline network device (e.g., the gas compressor). In some embodiments,the smart gas pipeline network safety management sub-platform mayinclude a plurality of modules, such as a pipeline network device safetymonitoring module, a safety emergency management module, a pipelinenetwork geographic information management module, and a pipeline networkrisk assessment management module, etc. The pipeline network devicesafety monitoring module may be configured to query historical safetydata and current safety operation data of the gas pipeline networkdevice, such as the gas compressor. The safety emergency managementmodule may be configured to form an emergency treatment plan accordingto safety risks of the pipeline network device. For example, the safetyemergency management module may formulate corresponding maintenance andrepair plans based on predicted safety hazards of the gas compressor.The pipeline network geographic information management module may beconfigured to view related data and geographic location information ofthe gas pipeline and the device in real-time and provide data supportfor on-site maintenance, repair, and other operations. The pipelinenetwork risk assessment management module may predict the safety hazardsof the pipeline network device based on a preset model by combining withbasic data (e.g., gas compressor parameters, etc.) of the pipelinenetwork, operation data of the pipeline network, etc. According to apredicted situation, a safety risk classification may be carried out,and 3D visual management of different colors may be carried out incombination with a Geographic Information System (GIS). For moreinformation on the prediction of the safety hazards of the pipelinenetwork device, such as the gas compressor, please refer to the relatedcontent below.

In some embodiments, the smart gas pipeline network safety managementsub-platform may further include a pipeline network inspection safetymanagement module, a pipeline network gas leakage monitoring module, asite inspection safety management module, a site gas leakage monitoringmodule, a site device safety monitoring module, a pipeline networksimulation management module, etc., which are not limited herein.

The smart gas data center is a platform for aggregating and storingvarious data, information, instructions, etc. For example, the smart gasdata center may store management data of various indoor and pipelinenetwork devices, operation data of various devices, various queryinstructions issued by users, etc.

In some embodiments, the smart gas pipeline network safety managementsub-platform may interact with the smart gas data center in a two-waymanner. For example, the smart gas pipeline network safety managementsub-platform may obtain/feedback the safety management data of the gascompressor and other pipeline network devices from the smart gas datacenter.

In some embodiments, the smart gas pipeline network safety managementsub-platform may maintain the pipeline network device, such as the gascompressor, through the smart gas data center. The smart gas data centermay send obtained relevant safety data to a corresponding pipelinenetwork device safety monitoring module by identifying a safetyparameter category (e.g., a usage amount, a usage duration)automatically. The pipeline network device safety monitoring module mayautomatically alarm when relevant safety parameters exceed a presetthreshold and choose to push alarm information to the user (e.g., thesupervision user) automatically.

In some embodiments, the smart gas safety management platform mayexchange information with the smart gas service platform and the smartgas pipeline network device sensor network platforms through the smartgas data center. In some embodiments, the data interaction of the smartgas safety management platform may include: the smart gas data centerreceiving a query instruction of gas pipeline network abnormalinformation issued by the smart gas service platform; the smart gas datacenter issuing instructions of obtaining the related data (e.g., thesafety management information of the gas compressor, etc.) of the gaspipeline network to the smart gas pipeline network device sensor networkplatform; the smart gas data center receiving the related data of thegas pipeline network uploaded by the smart gas pipeline network devicesensor network platform; the smart gas data center sending the relateddata of the gas pipeline network to the smart gas pipeline networksafety management sub-platform for analysis and processing; the smartgas pipeline network safety management sub-platform sending processeddata to the smart gas data center; and the smart gas data center sendingaggregated and processed data to the smart gas service platform. Theaggregated and processed data may include the safety managementinformation of the gas compressor, etc. For example, the aggregated andprocessed data may include the sound feature and vibration frequency ofthe gas compressor, etc.

In some embodiments, the smart gas safety management platform 130 mayprocess the safety management information of the compressor in the smartgas pipeline network uploaded by the smart gas pipeline network deviceobject platform 150. For example, when the abnormal information in thesafety management information of the compressor in the smart gaspipeline network exceeds a preset safety threshold, the smart gaspipeline network safety management sub-platform may alarm and pushwarning information to the user through the gas user sub-platformautomatically. The abnormal information may be abnormal sound of the gascompressor, abnormal vibration of the gas compressor, or the like.

The smart gas pipeline network device sensor network platform 140 is aplatform for obtaining the related data of the gas pipeline networkdevice, such as the gas compressor, which may be configured as acommunication network and a gateway. In some embodiments, the smart gaspipeline network device sensor network platform may be configured toimplement functions of network management, protocol management,instruction management, data analysis, etc. The network management is tomanage the network, which may realize the data and/or informationcirculation among the various platforms and modules. The protocolmanagement is to manage various networks and communication protocols,which may enable platforms and modules that execute different networksand communication protocols to exchange data and/or information. Theinstruction management is to manage various instructions (e.g.,instructions of obtaining the safety management information of thecompressor in the smart gas pipeline network) and may store and executethe various instructions. The data analysis is to analyze various data,instructions, etc., which may make each module and platform to berecognized or executed smoothly.

In some embodiments, the smart gas pipeline network device sensornetwork platform may be configured to interact downward with the smartgas pipeline network device object platform. For example, the smart gaspipeline network device sensor network platform may receive the datarelated to a pipeline network device uploaded by the smart gas pipelinenetwork device object platform, such as the safety managementinformation of the compressor in the smart gas pipeline network; andissue the instructions of obtaining data related to the pipeline networkdevice to the smart gas pipeline network device object platform, such asthe safety management information of the compressor in the smart gaspipeline network. In some embodiments, the smart gas pipeline networkdevice sensor network platform may also interact upward with the smartgas safety management platform. For example, the smart gas pipelinenetwork device sensor network platform may receive the instructions ofobtaining data related to the pipeline network device issued by thesmart gas data center, such as the safety management information of thecompressor in the smart gas pipeline network; and upload data related tothe pipeline network device to the smart gas data center, such as thesafety management information of the compressor in the smart gaspipeline network.

The smart gas pipeline network device object platform 150 may be aplatform for obtaining data and/or information related to the pipelinenetwork device. For example, the smart gas pipeline network deviceobject platform may be configured to obtain various indicators, such asthe sound feature and vibration frequency of the gas compressor. In someembodiments, the smart gas pipeline network device object platform maybe implemented based on a corresponding device terminal, such as the gascompressor, the gas pipeline, a flow meter, a pressure gauge, or thelike.

In some embodiments, the smart gas pipeline network device objectplatform may interact upward with the smart gas pipeline network devicesensor network platform. For example, the smart gas pipeline networkdevice object platform may receive the instructions of obtaining thedata related to the pipeline network device issued by the smart gaspipeline network device sensor network platform, such as the safetymanagement information of the compressor in the smart gas pipelinenetwork; and upload the data related to the safety managementinformation of the compressor in the smart gas pipeline network andother pipeline network devices to the smart gas pipeline network devicesensor network platform, etc.

In some embodiments of the present disclosure, the Internet of Thingssystem for safety management of the compressor in the smart gas pipelinenetwork is established, including the smart gas user platform, the smartgas service platform, the smart gas safety management platform, thesmart gas pipeline network device sensor network platform, and the smartgas pipeline network device object platform, which forms a closed loopof smart gas safety management information operation among pipelinenetwork devices, gas operators, gas users, and supervision users,thereby realizing informatization and intelligence of pipeline networksafety management and ensuring high-quality management effect.

It should be noted that the above description of the Internet of Thingssystem for the safety management of compressors in a smart gas pipelinenetwork and its modules is only for the convenience of description, anddoes not limit the present disclosure to the scope of the embodiments.It can be understood that after understanding the principle of thesystem, those skilled in the art may arbitrarily combine each module orform a subsystem to connect with other modules without departing fromthis principle. In some embodiments, the smart gas user platform, thesmart gas service platform, the smart gas safety management platform,the smart gas pipeline network device sensor network platform, and thesmart gas pipeline network device object platform disclosed in FIG. 1may be different modules in one system, or one module may realize thefunctions of the above two or more modules. For example, each module mayshare one storage module, and each module may also have its own storagemodule. All such variations are within the protection scope of thepresent disclosure.

FIG. 2 is a flowchart illustrating an exemplary method for safetymanagement of a compressor in a smart gas pipeline network according tosome embodiments of the present disclosure. In some embodiments, process200 may be implemented based on the Internet of Things system 100 forsafety management of the compressors in the smart gas pipeline network.The method may be executed by the smart gas safety management platform130. As shown in FIG. 2 , the process 200 includes the following steps.

Step 210: obtaining sound data and a target vibration feature of a gascompressor, and determining a target sound feature based on the sounddata.

The sound data refers to sound data and/or background noise when the gascompressor is operating. The background noise refers to a sum of ambientnoise contributed by sound sources other than a measured sound source.For example, the sound data may include one or more of sound producedwhen the gas compressor operates normally, sound produced when the gascompressor operates abnormally, and external ambient noise.

The target sound feature is a sound feature that removes the backgroundnoise in the sound data, and only retains the sound feature when the gascompressor works. For example, the target sound feature may be sounddata generated when the gas compressor works, a timbre feature of thesound generated when the gas compressor works, or the like.

The target vibration feature refers to a vibration feature when the gascompressor works. In some embodiments, the target vibration feature mayinclude a vibration amplitude, a vibration velocity, and an impact forceof the gas compressor.

In some embodiments, one or more sound sensors may be disposed at one ormore locations within a reasonable measured distance from the gascompressor to obtain sound data at one or more different locations. Thesmart gas safety management platform 130 may obtain the sound data fromthe one or more sound sensors.

In some embodiments, target vibration features of the gas compressor atone or more time points may be obtained based on one or more vibrationmeasurement sensors. The smart gas safety management platform 130 mayobtain target vibration features from the one or more vibrationmeasurement sensors.

In some embodiments, the smart gas safety management platform 130 maydetermine the target sound feature based on a purification process ofthe sound data. For more specific content of obtaining the target soundfeature, please refer to FIG. 3 and descriptions thereof below.

Step 220: obtaining gas data and device data, and determining a standardsound feature and a standard vibration feature based on the gas data andthe device data.

The gas data refers to data that may reflect the features of gas. Insome embodiments, the gas data may include one or more of the gas flow,gas pressure, gas type, gas impurity content, or the like. In someembodiments, the gas data may be converted into a gas data vector. Forexample, a gas data vector p may be constructed based on gas data of (x,y, m, n), and the gas data of (x, y, m, n) may represent the gas flow ofthe gas compressor is x, the gas pressure of the gas compressor is y,the gas type of the gas compressor is m, and the gas impurity content ofthe gas compressor is n.

The device data refers to data related to the performance of the gascompressor. In some embodiments, the device data may include one or moreof pattern, power, working year, and working status of the gascompressor, or the like. In some embodiments, the gas data may beconverted into a device data vector. For example, a device data vector kmay be constructed based on device data of (b, c, d, e), and the devicedata of (b, c, d, e) may represent that the pattern of the gascompressor is b, the power of the gas compressor is c, the working yearof the gas compressor is d, and the working state of the gas compressoris e.

In some embodiments, the gas data and the device data may be aggregatedand stored based on the smart gas data center on the smart gas safetymanagement platform 130.

The standard sound feature refers to a sound feature under thecorresponding gas data and device data when the gas compressor worksnormally.

The standard vibration feature refers to a vibration feature under thecorresponding gas data and device data when the gas compressor worksnormally. In some embodiments, the vibration feature may include thevibration amplitude, vibration velocity, and impact force under thecorresponding gas data and device data when the gas compressor worksnormally.

In some embodiments, the standard sound feature and the standardvibration feature may be identified and obtained by a machine learningmodel, obtained by calling a feature database where the standard soundfeature and the standard vibration feature are stored, obtained based ona rule input, or obtained based on other feasible manners.

In some embodiments, the smart gas safety management platform 130 mayestablish a feature data vector library based on networked data andreference device data. In some embodiments, the reference device datamay be data from gas compressors (the same devices that are produced andpurchased together) of the same type and the same batch. In someembodiments, the feature data vector library may include a reference gasdata vector and a reference device data vector, a reference soundfeature and reference vibration feature corresponding to the referencegas data vector, and a reference sound feature and reference vibrationfeature corresponding to the reference device data vector.

In some embodiments, the reference gas data vector and reference devicedata vector, the reference sound feature and reference vibration featurecorresponding to the reference gas data vector, and the reference soundfeature and reference vibration feature corresponding to referencedevice data vector may be constructed to obtain the feature data vectorlibrary based on the gas data and device data during a normal operationof the gas compressor in historical data and the sound feature andvibration feature during the normal operation of the gas compressor inthe historical data.

In some embodiments, the smart gas safety management platform 130 mayestablish the feature data vector library based on simulation. Forexample, based on actual data (e.g., the gas data and device data duringa normal operation of the gas compressor in historical data and thesound feature and vibration feature during the normal operation of thegas compressor in the historical data), the simulated gas data anddevice data, the sound feature and vibration feature corresponding tothe simulated gas data, and the sound feature and vibration featurecorresponding to the simulated device data may be obtained bysimulation. Based on the simulated gas data and device data, the soundfeature and vibration feature corresponding to the simulated gas data,and the sound feature and vibration feature corresponding to thesimulated device data, the smart gas safety management platform 130 mayconstruct the reference gas data vector and reference device datavector, the reference sound feature and reference vibration featurecorresponding to the reference gas data vector, and the reference soundfeature and the reference vibration feature corresponding to thereference device data vector to obtain the feature data vector library.

In some embodiments, the smart gas safety management platform 130 maycalculate a distance between the current gas data vector and thereference gas data vector and a distance between the current device datavector and the reference device data vector, respectively, and determinethe standard sound feature and the standard vibration featurecorresponding to the current gas data vector and the current device datavector. For example, the reference gas data vector and the referencedevice data vector whose distance between the current gas data vectorand the reference gas data vector satisfies a gas data preset condition,and the distance between the current device data vector and thereference device data vector satisfies a device data preset conditionmay be determined as the target vector. The reference sound feature andthe reference vibration feature corresponding to the reference gas datavector and the reference device data vector may be determined as thestandard sound feature and the standard vibration feature correspondingto the current data.

The preset condition may be set according to the actual situation. Thepreset condition may be a gas data preset condition or a device datapreset condition. For example, the preset condition may be that thevector distance is the smallest or the vector distance is less than adistance threshold, or the like. The vector distance may be the distancebetween the current gas data vector and the reference gas data vector,or the distance between the current device data vector and the referencedevice data vector. In some embodiments, the vector distance may becharacterized based on a cosine distance, or the like. In someembodiments, the distance threshold may be related to a distancedifference between the current data vector and the reference datavector, and the larger the distance difference is, the larger thedistance threshold may be.

In some embodiments of the present disclosure, a database is establishedfor vector matching, which may relatively quickly determine the standardsound feature and standard vibration feature of the current gascompressor under a normal operating condition, so as to quickly andaccurately predict the safety hazard of the gas compressor in thefuture, and improve the analysis efficiency. In addition, obtaining dataand establishing the database based on simulation can overcome theshortcomings of insufficient historical data or poor representativenessof historical data, so as to quickly determine the standard soundfeature and the standard vibration feature of the current gas compressorunder the normal operating condition, quickly and accurately predict thesafety hazard of the gas compressor in the future, and improve theanalysis efficiency.

In some embodiments, reference data of various sampling times andvarious sampling intervals (frequency) may be included in the featuredata vector library for usage and matching. Therefore, the sampling timeand sampling interval of the standard sound feature are the same as thesampling time and sampling interval of the sound data to ensure that thestandard sound feature obtained by sampling may match the preset soundfeature in the database.

Step 230: predicting whether there is a safety hazard in the gascompressor based on the target vibration feature and the standardvibration feature, or based on the target sound feature and the standardsound feature.

The safety hazard refers to a possibility and severity of gas compressorfailure.

In some embodiments, the smart gas safety management platform 130 maypredict whether there is a safety hazard in the gas compressor based onthe difference between the target vibration feature and the standardvibration feature, and/or based on the difference between the targetsound feature and the standard sound feature. For example, if thedifference between the target vibration feature and the standardvibration feature is greater than a preset distance threshold, it may bepredicted that there is a safety hazard in the gas compressor. Asanother example, if the difference between the target vibration featureand the standard vibration feature and the difference between the targetsound feature and the standard sound feature are less than the presetdistance threshold, it may be predicted that there is no safety hazardin the gas compressor.

In some embodiments, for more specific content about how to predictwhether there is a safety hazard in the gas compressor, see FIG. 4 anddescriptions thereof below.

In some embodiments, the smart gas safety management platform 130 mayissue a safety warning when it is predicted that there is a safetyhazard in the gas compressor. The safety warning may include one or moreof voice prompts, image prompts, or text prompts.

In some embodiments of the present disclosure, by excluding theinfluence of the background noise, the possibility and severity offailures can be predicted accurately and reasonably by jointly analyzingthe changes in the sound and vibration of the gas compressor andcombining with the gas data (e.g., the gas flow and gas pressure).

FIG. 3 is a schematic diagram illustrating an exemplary process fordetermining a target sound feature according to some embodiments of thepresent disclosure.

In some embodiments, the target sound feature may be determined based onthe purification process of the sound data, and the purification processincluding: processing the sound data by using a purification model toobtain the target sound feature.

The purification process refers to a processing method of removing thebackground noise in the sound data and retaining only the sound datawhen the gas compressor works.

In some embodiments, the purification process includes: obtaining sounddata at a plurality of different locations, and performing thepurification processing on the sound when the gas compressor works basedon the difference in sound at the different locations. For example, aplurality of sound sensors may be disposed at a plurality of locationswithin a reasonable measured distance from the gas compressor to extractthe sound data that is related to the distance based on the differencein the sound data at the different locations (e.g., the closer thedistance from the gas compressor is, the greater the sound amplitude ofthe gas compressor may be, the farther the distance from the gascompressor is, the smaller the sound amplitude of the gas compressor maybe).

In some embodiments, the target sound feature may also be determinedbased on the purification model 300. The purification model 300 is amodel for performing the purification process on the sound data toobtain the sound features when the gas compressor is working. In someembodiments, the purification model 300 may be a machine learning model.For example, the purification model 300 may be a neural network (NN)model, a deep neural network (DNN) model, or the like, or anycombination thereof.

As shown in FIG. 3 , the purification model 300 may include anextraction layer 320, a purification layer 340, or the like.

The extraction layer 320 may be configured to perform a featureextraction on a plurality of sets of sound data to obtain a plurality ofsets of initial sound features corresponding to the plurality of sets ofsound data. In some embodiments, the input of the extraction layer 320may include a plurality of sets of sound data 310, and the output of theextraction layer 320 may include a plurality of sets of initial soundfeatures 330 corresponding to the plurality of sets of sound data. Theplurality of sets of sound data may include sound data collected by theplurality of sound sensors disposed at different distances from the gascompressor. More descriptions of the sound data may be found in FIG. 2and related parts thereof.

The purification layer 340 may be configured to remove the sound featureof the background noise in the initial sound features to obtain thesound feature when the gas compressor works. In some embodiments, theinput of the purification layer 340 may include the initial soundfeatures 330 and the distance data 330-1, and the output of thepurification layer 340 may include the target sound feature 350. Thetarget sound feature 350 may be a sound feature that retains only thesound feature when the gas compressor works after removing thebackground noise in the sound data. For detailed descriptions of thetarget sound feature, please refer to FIG. 2 and related parts thereof.

The initial sound features 330 refer to sound features corresponding tounpurified sound data and may include the sound feature when the gascompressor works, the sound feature of the background noise, or thelike.

The distance data 330-1 refers to distance data from the gas compressorwhen the sound sensor collects the sound data. For example, the distancedata may be 1 meter, 2 meters, 3 meters from the sound sensor to the gascompressor, etc. In some embodiments, the plurality of sets of sounddata obtained by each of the plurality of sound sensors disposed atdifferent distances from the gas compressor may correspond to thedistance data at which the sound sensor is disposed.

In some embodiments, the purification model 300 may be obtained by jointtraining of the extraction layer 320 and the purification layer 340based on historical data.

The historical data may include a first training sample and a firsttraining label. The first training sample may include the sound data anddistance data of a plurality of locations in a plurality of sets ofhistorical data. The first training label may include the sound featureswhen the gas compressor works corresponding to the plurality sets ofhistorical data. In some embodiments, the first training sample and thefirst training label may be obtained based on historical work data ofthe gas compressor.

In some embodiments, the initial sound feature output by the extractionlayer may be configured as an input of the purification layer. Theprocess of the joint training may include: taking the historical sounddata as the input of the extraction layer; taking the initial soundfeatures and historical distance data output by the extraction layer asthe input of the purification layer to determine the output of thepurification model; inputting the target sound feature output by thepurification model and the training label into a loss function; anditeratively updating the purification model based on the loss functionand obtaining the trained purification model until the loss function isless than a threshold, converges, or the training period reaches athreshold, etc.

In some embodiments of the present disclosure, the smart gas safetymanagement platform may perform the purification process of the sounddata based on the purification model, which can accurately andefficiently obtain the sound feature when the gas compressor works,remove the interference of environmental noise, and improve theprediction effect of the safety hazard of the gas compressor.

FIG. 4 is a schematic diagram illustrating an exemplary process forpredicting a safety hazard probability of a gas compressor according tosome embodiments of the present disclosure.

In some embodiments, the safety hazard of the gas compressor may bemanually determined based on data, such as the working years of the gascompressor, the historical maintenance times of the gas compressor, orthe like. For example, it may be manually determined that a gascompressor that has worked for 7 years has a high safety hazard. Asanother example, it may be manually determined that a gas compressorwhose historical maintenance times are greater than 3 has a high safetyhazard.

In some embodiments, the safety hazard of the gas compressor may also bepredicted after processing the target vibration feature and the standardvibration feature, and/or the target sound feature and the standardsound feature by a hazard model.

In some embodiments, the hazard model may be a machine learning model.For example, the hazard model may be a neural network model, a deepneural network model, or the like, or any combination thereof.

As shown in FIG. 4 , the hazard model 400 may include a first matchinglayer 420, a second matching layer 430, a prediction layer 450, or thelike.

The first matching layer 420 is configured to determine a matchingdegree between the target sound feature and the standard sound feature.In some embodiments, an input of the first matching layer 420 mayinclude the standard sound feature 410-1, the target sound feature 350,and an output of the first matching layer 420 may include a soundmatching feature 440-1.

The sound matching feature refers to a feature of the matching degreebetween the standard sound feature and the target sound feature of thegas compressor. The smaller the matching degree between the target soundfeature and the standard sound feature is, the greater the safety hazardof the gas compressor may be. For relevant descriptions of the targetsound feature and the standard sound feature, please refer to FIG. 2 andrelated parts thereof.

The second matching layer 430 is configured to determine the matchingdegree of the standard vibration feature and the target vibrationfeature. In some embodiments, an input of the second matching layer 430may include the standard vibration feature 410-2 and the targetvibration feature 410-3, and an output of the second matching layer 430may include a vibration matching feature 440-3.

The vibration matching feature refers to a feature of the matchingdegree between the standard vibration feature and the target vibrationfeature of the gas compressor. The smaller the matching degree betweenthe target vibration feature and the standard vibration feature is, thegreater the safety hazard of the gas compressor may be. For relevantdescriptions of the target vibration feature and the standard vibrationfeature, please refer to FIG. 2 and related descriptions thereof.

The prediction layer 450 is configured to predict the safety hazardprobability of the gas compressor. In some embodiments, an input of theprediction layer 450 may include the sound matching feature 440-1 andthe vibration matching feature 440-3, and an output of the predictionlayer 450 may include the safety hazard probability 460.

The safety hazard probability refers to a probability of a safetyaccident taking place in the gas compressor. The greater the safetyhazard probability is, the greater the possibility of the safetyaccident taking place in the gas compressor may be. In some embodiments,the safety hazard probability may be expressed in various ways, such asa percentage. For example, the safety hazard probability may be 70%, orthe like.

In some embodiments, the input of the prediction layer 450 may alsoinclude the gas data 440-2.

More descriptions of the gas data may be found in FIG. 2 and relatedsections thereof.

FIG. 5 is a schematic diagram illustrating an exemplary process fortraining a hazard model according to some embodiments of the presentdisclosure.

In some embodiments, the hazard model may be determined by jointtraining of the first matching layer, the second matching layer, and theprediction layer.

As the schematic diagram of training a hazard model shown in FIG. 5 ,training process 500 includes an initial hazard model 510, a secondtraining sample 520, a second training label 530, and a trained hazardmodel 540.

The initial hazard model 510 refers to a model with no parameters setand may include an initial first matching layer, an initial secondmatching layer, and an initial prediction layer. The initial firstmatching layer, the initial second matching layer, and the initialprediction layer may be all models with no parameters set.

In some embodiments, the second training sample 520 may include a samplestandard sound feature, a sample standard vibration feature, a sampletarget sound feature, a sample target vibration feature, or the like. Insome embodiments, the sample target sound feature and the sample targetvibration feature may include features determined based on actualhistorical data and/or simulation data. The simulation data may bedetermined based on the simulation. Detailed descriptions of thesimulation may be found in FIG. 2 and related parts thereof. In someembodiments, the second training sample may further include sample gasdata, and the sample gas data may be obtained based on the historicaloperation data of the gas compressor. In some embodiments, the secondtraining sample may further include simulation data with a safety hazardand simulation data without a safety hazard.

In some embodiments, the second training label may be the probability ofa gas safety accident taking place in the corresponding second trainingsample. In some embodiments, the second training label 530 may bemanually labeled based on statistical data. In some embodiments, thesecond training label corresponding to different training data in thesecond training sample may be different. For example, for 100 identicaltraining samples, 60 samples with safety accidents are counted, then thelabel of the training samples may be manually labeled as 60%.

In some embodiments, the sound matching feature output by the firstmatching layer and the vibration matching feature output by the secondmatching layer may be determined as the input of the prediction layer.The process of the joint training may include: taking the samplestandard sound feature and the sample target sound feature in the secondtraining sample as the input of the first matching layer; taking thesample standard vibration feature and the sample target vibrationfeature as the input of the second matching layer; taking the soundmatching feature output by the first matching layer, the vibrationmatching feature output by the second matching layer, and the sample gasdata in the second training sample as the input of the prediction layerto determine the output of the hazard model; inputting the safety hazardprobability output by the hazard model and the second training labelinto a loss function; and iteratively updating the hazard model toobtain a trained hazard model 540 based on the loss function until theloss function is less than a threshold, converges, or the trainingperiod reaches a threshold, etc.

In some embodiments, the loss function may include a plurality of lossitems, including a simulation data item determined based on thesimulation data and/or an actual data item determined based on theactual historical data. The weight of the simulation data item is lessthan the weight of the actual data item. For example, if the lossfunction is square loss, when the number of samples is n, the lossfunction is:

L(Y,ƒ(X))=Σ_(i=1) ^(n)(Y−ƒ(X))².

The loss function may be constructed for each sample, and the item inthe loss function corresponding to each sample is the loss item. Thus,the loss function may include loss items of both the simulation data andthe actual historical data.

In some embodiments, the simulation data may be less difficult to obtainthan the actual historical data, the data amount of simulation data maybe larger, but the authenticity and reliability of simulation data maybe smaller. The accuracy of the hazard model prediction may be improvedby reducing the weight of the loss item corresponding to the simulationdata.

In one or more embodiments of the present disclosure, by establishingthe hazard model, the occurrence probability of different types ofsafety hazards of the gas compressor can be predicted relatively quicklybased on the sound feature and vibration feature. In addition, by usingthe simulation data to train the model, the defect of insufficienttraining data can be overcome to a certain extent, the model trainingefficiency can be improved, and the training cost can be reduced. Inaddition, when using the model for analysis, the current gas data may beconsidered as an input of the model so that the model prediction resultcan be more realistic.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose and that the appended claimsare not limited to the disclosed embodiments, but, on the contrary, areintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the disclosed embodiments. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution, e.g., an installation on an existing server or mobiledevice.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, numbers describing the number of ingredients andattributes are used. It should be understood that such numbers used forthe description of the embodiments use the modifier “about”,“approximately”, or “substantially” in some examples. Unless otherwisestated, “about”, “approximately”, or “substantially” indicates that thenumber is allowed to vary by ±20%. Correspondingly, in some embodiments,the numerical parameters used in the description and claims areapproximate values, and the approximate values may be changed accordingto the required characteristics of individual embodiments. In someembodiments, the numerical parameters should consider the prescribedeffective digits and adopt the method of general digit retention.Although the numerical ranges and parameters used to confirm the breadthof the range in some embodiments of the present disclosure areapproximate values, in specific embodiments, settings of such numericalvalues are as accurate as possible within a feasible range.

For each patent, patent application, patent application publication, orother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, or the like, the entirecontents of which are hereby incorporated into the present disclosure asa reference. The application history documents that are inconsistent orconflict with the content of the present disclosure are excluded, andthe documents that restrict the broadest scope of the claims of thepresent disclosure (currently or later attached to the presentdisclosure) are also excluded. It should be noted that if there is anyinconsistency or conflict between the description, definition, and/oruse of terms in the auxiliary materials of the present disclosure andthe content of the present disclosure, the description, definition,and/or use of terms in the present disclosure is subject to the presentdisclosure.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other variations may also fallwithin the scope of the present disclosure. Therefore, as an example andnot a limitation, alternative configurations of the embodiments of thepresent disclosure may be regarded as consistent with the teaching ofthe present disclosure. Accordingly, the embodiments of the presentdisclosure are not limited to the embodiments introduced and describedin the present disclosure explicitly.

What is claimed is:
 1. A method for safety management of a compressor ina smart gas pipeline network, wherein the method is implemented based ona smart gas safety management platform of an Internet of Things systemfor safety management of a compressor in a smart gas pipeline network,comprising: obtaining sound data and a target vibration feature of a gascompressor, and determining a target sound feature based on the sounddata; obtaining gas data and device data, and determining a standardsound feature and a standard vibration feature based on the gas data andthe device data; and predicting whether there is a safety hazard in thegas compressor based on the target vibration feature and the standardvibration feature, or based on the target sound feature and the standardsound feature.
 2. The method of claim 1, further comprising:establishing a feature data vector library based on networked data andreference device data, wherein the feature data vector library includesa reference gas data vector, a reference device data vector, a referencesound feature corresponding to the reference gas data vector, and areference vibration feature corresponding to the reference device datavector.
 3. The method of claim 1, wherein a sampling time and a samplinginterval of the standard sound feature are the same as a sampling timeand a sampling interval of the sound data.
 4. The method of claim 1,wherein the Internet of Things system for safety management of thecompressor in the smart gas pipeline network further includes: a smartgas user platform, a smart gas service platform, a smart gas pipelinenetwork device sensor network platform, and a smart gas pipeline networkdevice object platform; the smart gas user platform is configured toissue a query instruction for safety management information of gaspipeline network device to the smart gas service platform, and receivethe safety management information of gas pipeline network deviceuploaded by the smart gas service platform; the smart gas serviceplatform is configured to receive the query instruction for the safetymanagement information of gas pipeline network device issued by thesmart gas user platform; issue the query instruction for the safetymanagement information of gas pipeline network device to the smart gassafety management platform; and receive the safety managementinformation of gas pipeline network device uploaded by the smart gassafety management platform and upload the safety management informationto the smart gas user platform; the smart gas safety management platformis configured to receive the query instruction for the safety managementinformation of gas pipeline network device issued by the smart gasservice platform; issue an instruction for obtaining safety-related dataof gas pipeline network device to the smart gas pipeline network devicesensor network platform; receive and process the safety-related data ofgas pipeline network device uploaded by the smart gas pipeline networkdevice sensor network platform; and upload the safety managementinformation of gas pipeline network device to the smart gas serviceplatform; the smart gas pipeline network device sensor network platformis configured to receive the instruction for obtaining thesafety-related data of gas pipeline network device issued by the smartgas safety management platform; issue an instruction for obtainingoperation-related data of gas pipeline network device to the smart gaspipeline network device object platform; receive the operation-relateddata of gas pipeline network device uploaded by the smart gas pipelinenetwork device object platform; and upload the safety-related data ofgas pipeline network device to the smart gas safety management platform;and the smart gas pipeline network device object platform is configuredto receive the instruction for obtaining the operation-related data ofgas pipeline network device issued by the smart gas pipeline networkdevice sensor network platform; and upload the operation-related data ofgas pipeline network device to the smart gas pipeline network devicesensor network platform.
 5. The method of claim 1, wherein thedetermining a target sound feature based on the sound data includes:determining the target sound feature based on a purification process ofthe sound data, wherein the purification process includes: obtaining thetarget sound feature by using a purification model to process the sounddata.
 6. The method of claim 5, wherein the purification model is amachine learning model; the purification model includes an extractionlayer and a purification layer; an input of the extraction layerincludes the sound data, and an output of the extraction layer includesan initial sound feature; and an input of the purification layerincludes the initial sound feature and distance data, and an output ofthe purification layer includes the target sound feature.
 7. The methodof claim 5, wherein the method further includes: predicting the safetyhazard of the gas compressor by using a hazard model to process thetarget vibration feature and the standard vibration feature, or processthe target sound feature and the standard sound feature.
 8. The methodof claim 7, wherein the hazard model is a machine learning model,including a first matching layer, a second matching layer, and aprediction layer; an input of the first matching layer includes thetarget sound feature and the standard sound feature, and an output ofthe first matching layer includes a sound matching feature; an input ofthe second matching layer includes the target vibration feature and thestandard vibration feature, and an output of the second matching layerincludes a vibration matching feature; and an input of the predictionlayer includes the sound matching feature and the vibration matchingfeature, and an output of the prediction layer includes a safety hazardprobability corresponding to the gas compressor.
 9. The method of claim8, wherein training samples of the hazard model include a samplestandard sound feature, a sample standard vibration feature, a sampletarget sound feature, and a sample target vibration feature; the sampletarget sound feature and the sample target vibration feature include afeature determined based on actual historical data or simulation data,and the simulation data is determined based on a simulation; a label ofthe hazard model includes a sample safety hazard probabilitycorresponding to the gas compressor; a loss function is constructedbased on the sample safety hazard probability and the safety hazardprobability output by the hazard model after the training samples areinput, and parameters of the hazard model are updated to obtain atrained hazard model, wherein the loss function includes a plurality ofloss items, the plurality of loss items include a simulation data itemdetermined based on the simulation data or an actual data itemdetermined based on the actual historical data, and a weight of thesimulation data item is less than a weight of the actual data item. 10.The method of claim 7, wherein the input of the prediction layer furtherincludes the gas data, and the gas data includes one or more of a gasflow, a gas pressure, a gas type, or a gas impurity content.
 11. AnInternet of Things system for safety management of a compressor in asmart gas pipeline network, wherein the system comprises: a smart gassafety management platform; the smart gas safety management platform isconfigured to perform operations including: obtaining sound data and atarget vibration feature of a gas compressor, and determining a targetsound feature based on the sound data; obtaining gas data and devicedata, and determining a standard sound feature and a standard vibrationfeature based on the gas data and the device data; and predictingwhether there is a safety hazard in the gas compressor based on thetarget vibration feature and the standard vibration feature, or based onthe target sound feature and the standard sound feature.
 12. The systemof claim 11, wherein the smart gas safety management platform isconfigured to further perform operations including: establishing afeature data vector library based on networked data and reference devicedata, wherein the feature data vector library includes a reference gasdata vector, a reference device data vector, a reference sound featurecorresponding to the reference gas data vector, and a referencevibration feature corresponding to the reference device data vector. 13.The system of claim 11, wherein a sampling time and a sampling intervalof the standard sound feature are the same as a sampling time and asampling interval of the sound data.
 14. The system of claim 11, whereinthe system further includes: a smart gas user platform, a smart gasservice platform, a smart gas pipeline network device sensor networkplatform, and a smart gas pipeline network device object platform; thesmart gas user platform is configured to issue a query instruction forsafety management information of gas pipeline network device to thesmart gas service platform, and receive the safety managementinformation of gas pipeline network device uploaded by the smart gasservice platform; the smart gas service platform is configured toreceive the query instruction for the safety management information ofgas pipeline network device issued by the smart gas user platform; issuethe query instruction for the safety management information of gaspipeline network device to the smart gas safety management platform; andreceive the safety management information of gas pipeline network deviceuploaded by the smart gas safety management platform and upload thesafety management information to the smart gas user platform; the smartgas safety management platform is configured to receive the queryinstruction for the safety management information of gas pipelinenetwork device issued by the smart gas service platform; issue aninstruction for obtaining safety-related data of gas pipeline networkdevice to the smart gas pipeline network device sensor network platform;receive and process the safety-related data of gas pipeline networkdevice uploaded by the smart gas pipeline network device sensor networkplatform; and upload the safety management information of gas pipelinenetwork device to the smart gas service platform; the smart gas pipelinenetwork device sensor network platform is configured to receive theinstruction for obtaining the safety-related data of gas pipelinenetwork device issued by the smart gas safety management platform; issuean instruction for obtaining operation-related data of gas pipelinenetwork device to the smart gas pipeline network device object platform;receive the operation-related data of gas pipeline network deviceuploaded by the smart gas pipeline network device object platform; andupload the safety-related data of gas pipeline network device to thesmart gas safety management platform; and the smart gas pipeline networkdevice object platform is configured to receive the instruction forobtaining the operation-related data of gas pipeline network deviceissued by the smart gas pipeline network device sensor network platform;and upload the operation-related data of gas pipeline network device tothe smart gas pipeline network device sensor network platform.
 15. Thesystem of claim 11, wherein to determine a target sound feature based onthe sound data, the smart gas safety management platform is furtherconfigured to perform operations including: determining the target soundfeature based on a purification process of the sound data, wherein thepurification process includes: obtaining the target sound feature byusing a purification model to process the sound data.
 16. The system ofclaim 15, wherein the purification model is a machine learning model;the purification model includes an extraction layer and a purificationlayer; an input of the extraction layer includes the sound data, and anoutput of the extraction layer includes an initial sound feature; and aninput of the purification layer includes the initial sound feature anddistance data, and an output of the purification layer includes thetarget sound feature.
 17. The system of claim 15, wherein the smart gassafety management platform is further configured to perform operationsincluding: predicting the safety hazard of the gas compressor by using ahazard model to process the target vibration feature and the standardvibration feature, or process the target sound feature and the standardsound feature.
 18. The system of claim 17, wherein the hazard model is amachine learning model, including a first matching layer, a secondmatching layer, and a prediction layer; an input of the first matchinglayer includes the target sound feature and the standard sound feature,and an output of the first matching layer includes a sound matchingfeature; an input of the second matching layer includes the targetvibration feature and the standard vibration feature, and an output ofthe second matching layer includes a vibration matching feature; and aninput of the prediction layer includes the sound matching feature andthe vibration matching feature, and an output of the prediction layerincludes a safety hazard probability corresponding to the gascompressor.
 19. The system of claim 18, wherein training samples of thehazard model include a sample standard sound feature, a sample standardvibration feature, a sample target sound feature, and a sample targetvibration feature; the sample target sound feature and the sample targetvibration feature include a feature determined based on actualhistorical data or simulation data, and the simulation data isdetermined based on a simulation; a label of the hazard model includes asample safety hazard probability corresponding to the gas compressor; aloss function is constructed based on the sample safety hazardprobability and the safety hazard probability output by the hazard modelafter the training samples are input, and parameters of the hazard modelare updated to obtain a trained hazard model, wherein the loss functionincludes a plurality of loss items, the plurality of loss items includea simulation data item determined based on the simulation data or anactual data item determined based on the actual historical data, and aweight of the simulation data item is less than a weight of the actualdata item.
 20. The system of claim 17, wherein the input of theprediction layer further includes the gas data, and the gas dataincludes one or more of a gas flow, a gas pressure, a gas type, or a gasimpurity content.